Search Results for author:
Found 6 papers, 4 papers with code
Prompt-based Learning for Unpaired Image Captioning
Inspired by the success of Vision-Language Pre-Trained Models (VL-PTMs) in this research, we attempt to infer the cross-domain cue information about a given image from the large VL-PTMs for the UIC task.
Unpaired Image Captioning by Image-level Weakly-Supervised Visual Concept Recognition
The image-level labels are utilized to train a weakly-supervised object recognition model to extract object information (e. g., instance) in an image, and the extracted instances are adopted to infer the relationships among different objects based on an enhanced graph neural network (GNN).
Unsupervised Monocular Depth Perception: Focusing on Moving Objects
We first propose an outlier masking technique that considers the occluded or dynamic pixels as statistical outliers in the photometric error map.
Improving Contrastive Learning by Visualizing Feature Transformation
To our knowledge, this is the first attempt of its kind.
BORM: Bayesian Object Relation Model for Indoor Scene Recognition
First, we utilize an improved object model (IOM) as a baseline that enriches the object knowledge by introducing a scene parsing algorithm pretrained on the ADE20K dataset with rich object categories related to the indoor scene.
DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular Videos
Unsupervised learning of depth and ego-motion from unlabelled monocular videos has recently drawn great attention, which avoids the use of expensive ground truth in the supervised one.
Ranked #35 on
Monocular Depth Estimation
on KITTI Eigen split
